Simulating Gaussian boson sampling on graphs in polynomial time
By: Konrad Anand , Zongchen Chen , Mary Cryan and more
Potential Business Impact:
Lets computers solve some problems faster than before.
We show that a distribution related to Gaussian Boson Sampling (GBS) on graphs can be sampled classically in polynomial time. Graphical applications of GBS typically sample from this distribution, and thus quantum algorithms do not provide exponential speedup for these applications. We also show that another distribution related to Boson sampling can be sampled classically in polynomial time.
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